MULTILINGUAL TRAINING OF DEEP NEURAL NETWORKS

We investigate multilingual modeling in the context of a deep neural network (DNN) - hidden Markov model (HMM) hybrid, where the DNN outputs are used as the HMM state likelihoods. By viewing neural networks as a cascade of feature extractors followed by a logistic regression classifier, we hypothesise that the hidden layers, which act as feature extractors, will be transferable between languages. As a corollary, we propose that training the hidden layers on multiple languages makes them more suitable for such cross-lingual transfer. We experimentally confirm these hypotheses on the GlobalPhone corpus using seven languages from three different language families: Germanic, Romance, and Slavic. The experiments demonstrate substantial improvements over a monolingual DNN-HMM hybrid baseline, and hint at avenues of further exploration.